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ARS Home » Southeast Area » Auburn, Alabama » Aquatic Animal Health Research » Research » Publications at this Location » Publication #373807

Research Project: Integrated Research to Improve Aquatic Animal Health in Warmwater Aquaculture

Location: Aquatic Animal Health Research

Title: EasyParallel: a GUI platform for parallelization of STRUCTURE and NEWHYBRIDS analyses

Author
item ZHAO, HONGGANG - Auburn University
item Beck, Benjamin
item Fuller, Adam
item PEATMAN, ERIC - Auburn University

Submitted to: PLoS One
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/8/2020
Publication Date: 4/24/2020
Citation: Zhao, H., Beck, B.H., Fuller, S.A., Peatman, E. 2020. EasyParallel: a GUI platform for parallelization of STRUCTURE and NEWHYBRIDS analyses. PLoS One. 15(4):e0232110. https://doi.org/10.1371/journal.pone.0232110.
DOI: https://doi.org/10.1371/journal.pone.0232110

Interpretive Summary: The software programs STRUCTURE and NEWHYBRIDS are widely used population genetic programs useful in addressing questions related to genetic structure, population mixture, and hybridization. These programs usually require a large number of independent runs with many iterations to provide robust data for downstream analyses, thus significantly increasing computation time. Other programs such as Structure_threader and parallelnewhybrid were developed to address this problem by processing tasks at the same time on a multi-threaded processor; however, some advanced computer programming knowledge is required to run these programs. We developed our program, EasyParallel, as a community resource to facilitate practical and routine population structure and hybridization analyses. The way this program works, through multiple computer processing threads and at the same time (in parallel) allows processing of large genetic datasets in a very efficient way, with its point-and-click graphical user interface providing ready access to users who have little experience in script programming. Performance evaluation of EasyParallel using simulated datasets showed similar speed-up and parallel execution time when compared to Structure_threader and Parallelnewhybrid. EasyParallel is written in the programming language Python 3 and freely available on the GitHub site https://github.com/hzz0024/EasyParallel.

Technical Abstract: The software programs STRUCTURE and NEWHYBRIDS are widely used population genetic programs useful in addressing questions related to genetic structure, admixture, and hybridization. These programs usually require a large number of independent runs with many iterations to provide robust data for downstream analyses, thus significantly increasing computation time. Programs such as Structure_threader and parallelnewhybrid were previously developed to address this problem by processing tasks in parallel on a multi-threaded processor; however some programming knowledge (e.g., R, Bash) is required to run these programs. We developed EasyParallel as a community resource to facilitate practical and routine population structure and hybridization analyses. The multi-threaded parallelization of EasyParallel allows processing of large genetic datasets in a very efficient way, with its point-and-click GUI providing ready access to users who have little experience in script programming. Performance evaluation of EasyParallel using simulated datasets showed similar speed-up and parallel execution time when compared to Structure_threader and Parallelnewhybrid. EasyParallel is written in Python 3 and freely available on the GitHub site https://github.com/hzz0024/EasyParallel.